Research Papers: Applications

Antifriction Bearings Damage Analysis Using Experimental Data Based Models

[+] Author and Article Information
R. G. Desavale

e-mail: ramdesavale@rediffmail.com

R. Venkatachalam

e-mail: chalamrv@yahoo.com
Department of Mechanical Engineering,
National Institute of Technology,
Warangal 506 004, Andra Pradesh, India

S. P. Chavan

Department of Mechanical Engineering,
Walchand College of Engineering,
Sangli 416 415, Maharashtra, India
e-mail: chavan.walchand@gmail.com

Contributed by the Tribology Division of ASME for publication in the Journal of Tribology. Manuscript received September 12, 2012; final manuscript received April 16, 2013; published online August 6, 2013. Assoc. Editor: Xiaolan Ai.

J. Tribol 135(4), 041105 (Aug 06, 2013) (12 pages) Paper No: TRIB-12-1146; doi: 10.1115/1.4024638 History: Received September 12, 2012; Revised April 16, 2013

Diagnosis of antifriction bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a simple theory the different signal-enhancement and signal-analysis techniques. The experimental data based model (EDBM) has been pointed out as a key tool that is able to highlight the effect of possible damage in one of the bearing components within the vibration signal. This paper presents the application of the EDBM technique to signals collected on a test-rig, and be able to test damaged fibrizer roller bearings in different working conditions. The effectiveness of the technique has been tested by comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race, and rollers. Since EDBM performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This paper represents an original contribution of the paper.

Copyright © 2013 by ASME
Topics: Bearings , Vibration , Stress
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Fig. 1

Mathematical model of the antifriction bearing

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Fig. 5

Variation of amplitude with defects

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Fig. 7

Experimental setup

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Fig. 8

Waveform of LGB – H before balancing

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Fig. 4

Dimensionless variation of amplitude versus damping

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Fig. 6

Experimental setup

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Fig. 2

Dimensionless relationships between vibration amplitudes versus surface damage

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Fig. 3

Variation of amplitude with number of rollers

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Fig. 13

Waveform of UGB after balancing at full load

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Fig. 14

Waveform at TGB on full load

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Fig. 9

Waveform of UGB –H before balancing

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Fig. 10

Waveform of LGB – H after adding weight

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Fig. 11

Waveform of LGB after adding correction weight

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Fig. 12

Waveform at LGB after adding trial weight

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Fig. 15

Structure of three layered neural network

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Fig. 16

Number of weight versus error

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Fig. 17

Deflection versus stiffness

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Fig. 20

Defective bearings produce significantly more fault-index than healthy bearings



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